Diffusion tensor imaging (DTI) is a relatively new modality in Magnetic Resonance Imaging (MRI). The DTI Magnetic Resonance Imaging (DTI-MRI) technique is used because of the capability to depict white matter tracts and for its sensitivity to micro-structural and architectural features of brain tissue. As such, because of the ability of DTI-MRI to delineate detailed anatomy of the white matter, it is becoming a widely-used imaging method in research and clinical studies.
According to this method, diffusion constant of water molecules are measured along multiple orientations. If the imaged sample has ordered anatomical structures, such as axonal bundles in the brain, water tends to diffuse along such structures. Consequently, results of diffusion constant measurements of the brain are not the same depending on the orientations of the measurements. This is called anisotropic diffusion. The extent of the anisotropy and the orientation of the largest diffusion constant, assuming that it represent the orientations of the axonal bundles, carry unique anatomical information, which has not been accessible by conventional MRI.
In the conventional DTI-MRI method, diffusion tensor maps are typically computed by fitting the signal intensities from diffusion weighted images as a function of their corresponding b-matrices [Mattiello J, Basser P J, Le Bihan D. The b matrix in diffusion tensor echo-planar imaging. Magn Reson Med 1997; 37:292-300] according to the multivariate least-squares regression model proposed by Basser et al. [Basser P J, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 1994; 103: 247-254]. The least-squares (LS) regression model takes into account the signal variability produced by thermal noise by including the assumed signal variance as a weighting factor in the tensor fitting.
While it is a highly useful MRI technique to investigate normal and abnormal brain anatomy, it has several drawbacks. Signal variability in diffusion weighted imaging (DWI) is influenced not only by thermal noise but also by spatially and temporally varying artifacts. Such artifacts originate from the so called “physiologic noise” such as subject motion and cardiac pulsation, as well as from acquisition-related factors such as system instabilities. The multivariate least-squares regression model assumes that the signal variability in the DWI is affected only by thermal noise and does not account for signal perturbations and potential outliers that originate from artifacts. While the signal variability produced by thermal noise is approximately Gaussian distributed [Henkelman R M. Measurement of signal intensities in the presence of noise in MR images. Med Phys 1985; 12:232-233], signal variability produced by physiologic noise and other artifacts does not have a known parametric distribution and thus currently cannot be modeled.
For example, one of the most significant issues is the technique's sensitivity to tissue motion. Because water molecules moves 5-10 μm during the measurement and the image contrast is sensitized to such a small motion, the image could be easily corrupted by even a small amount of brain motion, such as cardiac pulsation. To ameliorate this issue, single-shot rapid imaging is usually used for DTI-MRI data acquisition. However, even with such rapid imaging, it is very common that the dataset contains several corrupted images such as that shown in, for example, FIG. 1A.
In the DTI technique only 7 images (one non-diffusion weighted image and six different diffusion weighting orientations) are required to perform the diffusion tensor fitting. However, to enhance signal-to-noise ratio (SNR) one acquires raw images far more than necessary; for the tensor fitting, typically on the order of 12-90 images are acquired by repeating the six different diffusion weighted imaging conditions or by acquiring diffusion weighted images with more than six different weighting orientations.
A couple of techniques or schemes have been described which use or apply a routine fitting outlier detection scheme to DTI-MRI. These techniques have been described as the use of “robust” estimators, which are less sensitive to the presence of outliers. One robust tensor estimation approach, as proposed by Mangin et al. [Mangin J F, Poupon C, Clark C, Le Bihan D, Bloch I. Distortion correction and robust tensor estimation for MR diffusion imaging. Med Image Anal 2002; 6:191-198], is based on the well-known Geman-McClure M-estimator [Geman S, McClure D E. Statistical methods for tomographic image reconstruction. Bull Int Stat Inst 1987; 52:5-21]. Mangin's approach is referred to herein as GMM. This approach uses an iteratively re-weighted least-squares fitting in which the weight of each data point is set to a function of the residuals of the previous iteration. The GMM method ensures that potentially artifactual data points having large residuals are given lower weights in the estimation of the tensor parameters. Clearly, this approach is statistically more robust than the standard least squares methods in the presence of outliers. However, by using the residuals as the only determinants of the weights, it discards the information contained in the known distribution of errors related to thermal noise.
The other technique or “robust” estimator for robust diffusion tensor estimation, referred to as RESTORE (for robust estimation of tensors by outlier rejection) uses iteratively re-weighted least-squares regression to identify potential outliers and subsequently exclude them. Results from both simulated and clinical diffusion data sets indicate that the RESTORE method improves tensor estimation compared to the commonly used linear and nonlinear least-squares tensor fitting methods and a recently proposed method based on the Geman-McClure M-estimator. The RESTORE method could potentially remove the need for cardiac gating in DWI acquisitions and should be applicable to other MR imaging techniques that use univariate or multivariate regression to fit MRI data to a model [Lin-Ching Chang, Derek K. Jones, and Carlo Pierpaoli, “RESTORE: Robust Estimation of Tensors by Outlier Rejection”, Magn. Reson. Med. 53: 1088-1095 (2005)].
As is known to those skilled in the art, MRI data of a volume is usually acquired by essentially dividing the volume into a plurality of slices and performing 2-D imaging in each slice. When performing DTI-MRI, one would acquire one non-diffusion image and the 6 or more diffusion weighted images for each slice, where one slice of MR images typically consists of 64×64˜256×256 pixels. The tensor fitting is performed for every pixel independently and in the RESTORE technique, the handling of outliers, is done for each pixel separately as well. In other words, in RESTORE one estimates the tensor in the presence of data and does not attempt to eliminate corrupted images. As can from a comparison of the corrupted and uncorrupted images of FIGS. 1A and B, when an image is corrupted the corruption for example by tissue motion does not affect the image each pixel independently but rather there are clusters of pixels that that loose or gain abnormal pixel intensity. This is simply because tissue motion happens independent of the pixel size.
There also is a manual technique in which every image that is acquired is visually inspected by a clinician or person knowledgeable in interpreting the particular anatomical images to determine if any of the images display corruption. If corruption of an image is detected, the clinician can exclude the acquired image from being used further in the DTI-MRI process. Needless to say the visual inspection process is time consuming and subject to error given the large number of images that would have to be evaluated when SNR is taken into consideration. For example, if the volume was divided into 50 slices and 60 diffusion weighted images where acquired for each slice, then about 3000 (50×60) images would be acquired and evaluated. There presently is no existing technique by which corrupted images are detected other than by such a manual visual inspection process.
It thus would be desirable to provide methods and MRI systems that automatically evaluate the quality of the acquired raw images and remove corrupted images from the subsequent tensor fitting. It also would be particularly desirable to provide such methods and MRI systems that would allow such an evaluation to be performed post acquisition of image data and/or during the image data acquisition process (i.e., essentially in real time). It also would be particularly desirable to provide such methods and MRI systems that would allow the image acquisition process to acquire additional image data to replace image data that is determined to be corrupted by such an evaluation. It also would be particularly desirable to provide MRI systems that embody computer applications programs or computer implemented methods that embody such methods that automatically evaluate the quality of the acquired raw images and remove corrupted images from the subsequent tensor fitting. It also would be desirable to provide such methods and MRI systems that would not require users having greater skills or abilities than those who routinely perform MRI processed and operate magnetic resonance imaging apparatuses and systems.